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future. This post-doctoral position will investigate the use of knowledge graphs on automatically extracted metadata at a cross-disciplinary global scale. Using LLMs and traditional data engineering
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AI can accelerate provenance research processes, including especially the use of Large Language Model based transcription of printed and handwritten documents, and subsequent extraction of knowledge
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lipids to visualize and measure inter-organelle trafficking and metabolism of lipids in single cells. This position requires knowledge of the endomembrane system and strong skills in molecular biology and
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with IMPACT-Y leadership, Drs. Sarah Yip, Chris Pittenger and Godfrey Pearlson. This work will involve: Implementing and validating computational models of cognition and behavior (e.g., reinforcement
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, pharmacoepidemiology, biostatistics, or a closely related quantitative discipline. Working knowledge of core pharmacoepidemiological concepts, in particular confounding by indication, propensity score approaches, and
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with in-depth interviews on sensitive topics particularly welcome. Demonstrated ability to work in multidisciplinary research teams. Background knowledge of data science and/or AI technologies
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human genetics and/or statistics, including knowledge of genomewide methods, such as GWAS and post-GWAS analysis, and comfort with bioinformatics. This is highly collaborative work, so the candidate
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, development, and maintenance of online research databases using REDCap Use of electronic medical record systems (e.g., EPIC) and large claim/surveillance datasets for clinical research Develop domain knowledge
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Medicine at Yale School of Medicine in New Haven, Connecticut. Qualifications Required Qualifications PhD, MD/PhD, or equivalent doctoral degree in any field of knowledge by the start date Strong background
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on career development strategies. We will select highly qualified candidates who wish to pursue a career in CPC. It is our goal that CPC Fellows will develop the knowledge and skills necessary to function